Martina K. Brueckner

This study uses AI to show that genome sequencing can predict adverse outcomes after congenital heart defect surgery by identifying damaging genotypes in specific genes.

The paper shows that endocardial primary cilia and blood flow regulate EndoMT during cushion development by modulating KLF4 expression.

This study shows that inactivating Nphp2 in renal epithelial cells causes nephronophthisis-like phenotypes in mice, including cysts and fibrosis, and that cilia play a role in this process.

The paper uses a Markov Random Field model with protein-protein interaction data to identify 46 candidate genes for congenital heart disease, including known and highly expressed genes.

This paper introduces EncoreDNM, a statistical framework to quantify shared genetic effects of de novo mutations between disorders using exome-wide summary data, revealing abundant pairwise enrichment correlations.

The paper identifies mosaic mutations in blood and cardiac tissue from CHD patients, finding 1% of cases have detectable mosaic variants that may contribute to congenital heart disease.

The paper introduces MEM, a method to identify deletion CNVs in WES and WGS trio data by clustering Mendelian errors, achieving high validation rates and detecting additional de novo deletions and contamination.